# proposals: MCMC proposal distributions In pomp: Statistical Inference for Partially Observed Markov Processes

 proposals R Documentation

## MCMC proposal distributions

### Description

Functions to construct proposal distributions for use with MCMC methods.

### Usage

```mvn.diag.rw(rw.sd)

mvn.rw(rw.var)

rw.sd,
rw.var,
scale.start = NA,
scale.cooling = 0.999,
shape.start = NA,
target = 0.234,
max.scaling = 50
)
```

### Arguments

 `rw.sd` named numeric vector; random-walk SDs for a multivariate normal random-walk proposal with diagonal variance-covariance matrix. `rw.var` square numeric matrix with row- and column-names. Specifies the variance-covariance matrix for a multivariate normal random-walk proposal distribution. `scale.start, scale.cooling, shape.start, target, max.scaling` parameters to control the proposal adaptation algorithm. Beginning with MCMC iteration `scale.start`, the scale of the proposal covariance matrix will be adjusted in an effort to match the `target` acceptance ratio. This initial scale adjustment is “cooled”, i.e., the adjustment diminishes as the chain moves along. The parameter `scale.cooling` specifies the cooling schedule: at n iterations after `scale.start`, the current scaling factor is multiplied with `scale.cooling^n`. The maximum scaling factor allowed at any one iteration is `max.scaling`. After `shape.start` accepted proposals have accumulated, a scaled empirical covariance matrix will be used for the proposals, following Roberts and Rosenthal (2009).

### Value

Each of these calls constructs a function suitable for use as the `proposal` argument of `pmcmc` or `abc`. Given a parameter vector, each such function returns a single draw from the corresponding proposal distribution.

### Author(s)

Aaron A. King, Sebastian Funk

### References

\Roberts

2009

More on Markov chain Monte Carlo methods: `approximate Bayesian computation`, `pmcmc()`